Publications: Learning for Recommender Systems

Recommender systems suggest information sources and products to users based on
learning from examples of their likes and dislikes. Most existing recommender
systems use collaborative filtering methods that base recommendations on other
users' preferences. By contrast, content-based methods use information about an
item itself to make suggestions. This approach has the advantage of being able
to recommended previously unrated items to users with unique interests and to
provide explanations for its recommendations. Our work has focused on a
content-based book recommending system called LIBRA. We have also explored combining
our content-based approach and standard collaborative filtering.

Explaining Recommendations: Satisfaction vs. Promotion[Details] [PDF] Mustafa Bilgic and Raymond J. MooneyIn Proceedings of Beyond Personalization 2005: A Workshop on the Next Stage of Recommender Systems Research at the 2005 International Conference on Intelligent User Interfaces, San Diego, CA, January 2005.